Merge pull request #60 from PlasmaControl/main

Update the test structure in dev branch.

Author: renierts

.github/workflows/python-test-push.yml

@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9]  # 3
+        python-version: [3.9, 3.11, 3.12]  # 3
     steps:
       - uses: actions/checkout@v2
       - name: Set up Python ${{ matrix.python-version }}
@@ -18,8 +18,8 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install pytest==5.4.1 flake8==4.0.1 pytest-flake8 mypy pytest-mypy pytest-cov \
-          pytest-pep257 types-setuptools
+          pip install pytest flake8 pytest-flake8-v2 mypy pytest-mypy pytest-cov \
+          types-setuptools
           if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
       - name: Lint with flake8
         run: |

README.md

@@ -39,10 +39,10 @@ Please see the [References section](#references) for more information. For code
 
 ### Prerequisites
 
-PyRCN is developed using Python 3.7 or newer. It depends on the following packages:
+PyRCN is developed using Python 3.9 or newer. It depends on the following packages:
 
 - [numpy>=1.18.1](https://numpy.org/)
-- [scikit-learn>=1.0](https://scikit-learn.org/stable/)
+- [scikit-learn>=1.4](https://scikit-learn.org/stable/)
 - [joblib>=0.13.2](https://joblib.readthedocs.io)
 - [pandas>=1.0.0](https://pandas.pydata.org/)
 - [matplotlib](https://matplotlib.org/)

pytest.ini

@@ -1,17 +1,18 @@
 # pytest.ini
 [pytest]
-minversion = 5.0
+minversion = 6.5.0
 addopts =
     -ra -q -v
     --doctest-modules
-    # --junitxml=junit/test-results.xml
-    # --cov=src/pyrcn
-    # --cov-branch
-    # --cov-report=xml
-    # --cov-report=html
-	# --cov-report=term-missing
+    --junitxml=junit/test-results.xml
+    --cov=src/pyrcn
+    --cov-branch
+    --cov-report=xml
+    --cov-report=html
+	--cov-report=term-missing
+    # --pep257
     # --flake8
-    # --mypy
+    --mypy
 
 testpaths =
     src/pyrcn

requirements.txt

@@ -0,0 +1,12 @@
+setuptools>=46.4.0
+scikit-learn>=1.4
+numpy>=1.18.1
+scipy>=1.4.0
+joblib>=0.13.2
+pandas>=1.0.0
+typing-extensions
+requests[matplotlib]
+requests[seaborn]
+requests[ipywidgets]
+requests[ipympl]
+requests[tqdm]

setup.cfg

@@ -0,0 +1,32 @@
+[metadata]
+name = PyRCN
+version = 0.0.18
+author = Peter Steiner
+author_email = [email protected]
+description = A scikit-learn-compatible framework for Reservoir Computing in Python
+long_description = file: README.md
+long_description_content_type = text/markdown
+url = https://github.com/TUD-STKS/PyRCN
+project_urls =
+    Documentation = https://pyrcn.readthedocs.io/
+    Funding = https://pyrcn.net/
+    Source = https://github.com/TUD-STKS/PyRCN/
+    Bug Tracker = https://github.com/TUD-STKS/PyRCN/issues
+classifiers =
+    Programming Language :: Python :: 3
+    Development Status :: 2 - Pre-Alpha
+    License :: OSI Approved :: BSD License
+    Operating System :: OS Independent
+    Intended Audience :: Science/Research
+
+[options]
+package_dir =
+    = src
+packages = find:
+python_requires = >=3.9
+
+[options.packages.find]
+where = src
+
+[tool:pytest]
+testpaths = tests

src/pyrcn/base/_base.py

@@ -91,17 +91,14 @@ def _make_sparse(k_in: int, dense_weights: np.ndarray,
         The sparse layer weights
     """
     n_inputs, n_outputs = dense_weights.shape
-    nr_entries = int(n_inputs * k_in)
-    indices = np.zeros(shape=nr_entries, dtype=int)
-    indptr = np.arange(start=0, stop=(n_inputs + 1) * k_in, step=k_in)
-    dense_weights = dense_weights.flatten()[:nr_entries]
-
-    for en in range(0, n_inputs * k_in, k_in):
-        indices[en: en + k_in] = \
-            random_state.permutation(n_outputs)[:k_in].astype(int)
-    return scipy.sparse.csr_matrix(
-        (dense_weights, indices, indptr), shape=(n_inputs, n_outputs),
-        dtype='float64')
+
+    for neuron in range(n_outputs):
+        all_indices = np.arange(n_inputs)
+        keep_indices = np.random.choice(n_inputs, k_in, replace=False)
+        zero_indices = np.setdiff1d(all_indices, keep_indices)
+        dense_weights[zero_indices, neuron] = 0
+
+    return scipy.sparse.csr_matrix(dense_weights, dtype='float64')
 
 
 def _normal_random_weights(

src/pyrcn/model_selection/_search.py

@@ -8,7 +8,7 @@
 
 from sklearn.base import BaseEstimator, is_classifier, clone
 from sklearn.model_selection._search import BaseSearchCV
-from sklearn.utils.validation import indexable, _check_fit_params
+from sklearn.utils.validation import indexable, _check_method_params
 from sklearn.model_selection._split import check_cv
 
 import numpy as np
@@ -510,7 +510,7 @@ def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None, *,
         func = self.func
 
         X, y, groups = indexable(X, y, groups)
-        fit_params = _check_fit_params(X, fit_params)
+        fit_params = _check_method_params(X, fit_params)
 
         cv_orig = check_cv(self.cv, y, classifier=is_classifier(estimator))
         n_splits = cv_orig.get_n_splits(X, y, groups)

tests/test_esn.py

@@ -127,38 +127,38 @@ def test_esn_output_unchanged() -> None:
     esn = ESNClassifier(hidden_layer_size=50).fit(X, y)
     print(esn)
     shape2 = y[0].shape
-    assert(shape1 == shape2)
+    assert (shape1 == shape2)
 
 
 def test_esn_classifier_sequence_to_value() -> None:
     X, y = load_digits(return_X_y=True, as_sequence=True)
     esn = ESNClassifier(hidden_layer_size=50).fit(X, y)
     y_pred = esn.predict(X)
-    assert(len(y) == len(y_pred))
-    assert(len(y_pred[0]) == 1)
-    assert(esn.sequence_to_value is True)
-    assert(esn.decision_strategy == "winner_takes_all")
+    assert (len(y) == len(y_pred))
+    assert (len(y_pred[0]) == 1)
+    assert (esn.sequence_to_value is True)
+    assert (esn.decision_strategy == "winner_takes_all")
     y_pred = esn.predict_proba(X)
-    assert(y_pred[0].ndim == 1)
+    assert (y_pred[0].ndim == 1)
     y_pred = esn.predict_log_proba(X)
-    assert(y_pred[0].ndim == 1)
+    assert (y_pred[0].ndim == 1)
     esn.sequence_to_value = False
     y_pred = esn.predict(X)
-    assert(len(y_pred[0]) == 8)
+    assert (len(y_pred[0]) == 8)
     y_pred = esn.predict_proba(X)
-    assert(y_pred[0].ndim == 2)
+    assert (y_pred[0].ndim == 2)
     y_pred = esn.predict_log_proba(X)
-    assert(y_pred[0].ndim == 2)
+    assert (y_pred[0].ndim == 2)
 
 
 def test_esn_classifier_instance_fit() -> None:
     X, y = load_digits(return_X_y=True, as_sequence=True)
     esn = ESNClassifier(hidden_layer_size=50).fit(X[0], np.repeat(y[0], 8))
-    assert(esn.sequence_to_value is False)
+    assert (esn.sequence_to_value is False)
     y_pred = esn.predict_proba(X[0])
-    assert(y_pred.ndim == 2)
+    assert (y_pred.ndim == 2)
     y_pred = esn.predict_log_proba(X[0])
-    assert(y_pred.ndim == 2)
+    assert (y_pred.ndim == 2)
 
 
 def test_esn_classifier_partial_fit() -> None:

tests/test_model_selection.py

@@ -31,13 +31,13 @@ def test_sequentialSearchCV_equivalence() -> None:
             ('gs3', GridSearchCV, param_grid1)]).fit(X, y)
     assert gs1.best_params_ == ss.all_best_params_['gs1']
     assert gs2.best_params_ == ss.all_best_params_['gs2']
-    assert(isinstance(ss.cv_results_, dict))
-    assert(ss.best_estimator_ is not None)
-    assert(isinstance(ss.best_score_, float))
+    assert (isinstance(ss.cv_results_, dict))
+    assert (ss.best_estimator_ is not None)
+    assert (isinstance(ss.best_score_, float))
     print(ss.best_index_)
-    assert(isinstance(ss.n_splits_, int))
-    assert(isinstance(ss.refit_time_, float))
-    assert(isinstance(ss.multimetric, bool))
+    assert (isinstance(ss.n_splits_, int))
+    assert (isinstance(ss.refit_time_, float))
+    assert (isinstance(ss.multimetric, bool))
 
 
 @pytest.mark.skip(reason="no way of currently testing this")